Identification of glycolysis -correlative features for predicting prognosis and investigating immune landscape in prostate cancer

Prostate cancer (PCa) is characterized not merely as a malignant tumor, but also as a metabolic disorder encompassing dysregulation of glycolysis. This study was purposed to develop a new effective prognostic model correlated with glycolysis-related genes (GRGs) and investigate its potential mechanisms in PCa. We compared the expression differences of GRGs. A glycolysis-associated prognostic model was then developed to categorize PCa patients into different risk subgroups. The diagnostic accuracy and predictive efficacy of the models were assessed. Furthermore, a comprehensive nomogram was developed, incorporating the risk score feature, T and N stage, Gleason score, and age, which was further calibrated for accuracy. Risk groups were analyzed for correlation with tumor-infiltrating immune cells (TIICs), immune function, and immunotherapy. In addition, we performed functional enrichment analyses. Through constructing Cytoscape regulatory networks, 10 hub genes were identified, and their significance was evaluated and validated. As a result, we confirmed 12 genes (B3GALT6, ANKZF1, IDUA, ENO2, ALDH1A3, GUSB, AURKA, CDK1, LDHB, ALDH3B2, GALM, and ADH1C) for prognostic modeling and calculation of risk scores. Mutations, TIICs, and drug sensitivity were also analyzed. Furthermore, the PTTG1, associated with glycolysis and tumor immunity, was confirmed in vivo. Overall, these findings underscore the prognostic relevance of glycolysis-related genes in prostate cancer and provide novel insights into their association with disease progression and the tumor immune microenvironment.